Multi-tasking is a term that appears to be more prevalent in the vernacular than ever before. While the term multi-tasking may be thought of as a term that implies performing two disparate tasks (e.g., walking and participating in a text message conversation), in actuality many singular tasks themselves can be subdivided into multiple tasks (e.g., walking involves commanding legs to move, determining and moving in a particular direction, assessing the surrounding environment to avoid potential hazards, etc.). As access to information, and the types of information available, continues to increase, singular tasks are more often sub-dividable into multiple tasks because individuals performing a singular task do so by assessing multiple resources of information and acting upon that information. Not all information, however, is created equal. Some information is more helpful to the performance of a singular task than some other information, and some information may even distract and/or detract from an individual's ability to perform that singular task.
Driving is an example of a task that requires an individual, also referred to as operator of a vehicle or driver, to monitor multiple information sources over an extended period of time. Specifically, driving calls for the management of attention to many sources of information (e.g., visual, auditory, and other), and to the provision of multiple control inputs to the vehicle. Additionally, potential distractions at various locations relative to the driver's seat may compete for the driver's sensory, perceptual, and attentional resources, such as the immediate road and its condition (e.g., potholes, precipitation on the road, lane closures, speed limit signs), the immediate road surroundings (e.g., traffic, hazards, pedestrians, construction and its associated materials), general surroundings (e.g., weather, billboards), in-cab instrumentation (e.g., instrument clusters, infotainment centers, Global Positioning System devices, alerts, mirrors), passengers, and other objects (e.g., cell phones, beverages). Today, more than ever, drivers are faced with increasing competition for their attention due to the presence of in-vehicle information systems (e.g., instrument clusters, infotainment centers, etc.), cellular connectivity applications in modern vehicles, satellite navigation systems, and smartphone applications, among other information resources. Furthermore, naturally occurring distracting activities, such as conversations by way of cellular phones, conversations with passengers, listening to the radio, mind-wandering, and roadside advertising, can also compete to draw the driver's attention away from the road. Notably, even for autonomous or semi-autonomous cars, these same challenges of resource allocation are prevalent as the system (e.g., one or more processors thereof) receives and responds to the various information sources to decide which actions to take while driving.
In view of the above, drivers (individuals and/or processors associated with a vehicle) must manage their attention to and from the roadway, deciding when, where, and for how long to remove their attention from the road. In light of the continually increasing volume of in-vehicle and out-of-vehicle attention-grabbing sources, there is rising concern that certain types and magnitudes of task demands on the driver may selectively impair elements of driving. These task loads may be sensory, perceptual, motoric, cognitive, mixed, etc. Systems and methods aimed to combat the pitfalls of distracted driving exist, but they suffer from many deficiencies. For example, some distracted driving detection techniques provide for a binary detection of distracted driving, determining the presence or absence of distracted driving based on negative driver behaviors that lead to adverse events (e.g., looking “off-road” for a certain amount of time). Upon identification of a distracted driving state, existing systems may take restrictive action with respect to in-vehicle systems to account for the distracted driving state, such as applying the brakes to slow the vehicle down. Such a binary system can be very rigid though, and may fail to account for various levels of distractions that may merit various levels of responses. Existing systems, like some binary systems, also fail to account for positive features of a driver's behavior, such as road scanning, which also play a role in the driver's resource allocation management.
As indicated above, managing multiple streams of information from various locations is a task not limited to driving. A person skilled in the art will appreciate that many tasks exist that require a person to balance multiple forms of information, assess the situation based on that information, and respond accordingly. This is particularly true in situations that involve dynamic engagement with a system and an uncertain environment in which multi-tasking is part of the primary task, and/or when both task-relevant and task-irrelevant activities are possible. Non-limiting examples of such tasks include walking, bicycling, flying, operating heavy machinery, and operating other modes of transportation, including surface transportation (e.g., operating trucks, buses, trains, subways, military vehicles such as tanks, etc.), maritime transportation (e.g., operating boats, submarines, etc.), and aerial transportation (e.g., operating airplanes, helicopters, dirigibles, etc.). The difficulties in balancing multiple forms of information is also not limited to vehicle operation and the like, as it impacts many process controls—particularly those with a fairly complex panel of controls and displays. By way of non-limiting examples, process controls that involve balancing multiple forms of information include operating any of nuclear energy facilities, manufacturing control facilities, flight control facilities, space mission control, communications control, etc. As the world becomes more connected, this information balancing act may involve either or both a person and a processor or the like, either or both of which may be involved in performing this balancing act. The processor may be part of an object with which the person is interacting—a vehicle and smartphones are two such objects—and/or it may part of a standalone computer, network of computers, etc. that gather and assess large amounts of data.
Accordingly, there is a need for systems and methods that better account for a system's and/or person's (e.g., driver's) task demand and attentional resource allocation. Such demands of task load may be multimodal, requiring multiple input modalities (e.g., vision, hearing, touch) and/or multiple output modalities (e.g., motor movements, speech, etc.), as well as requiring cognitive processing resources with the person. Improved systems and methods for accounting for the demands of tasks and attentional allocation would enhance the design of related interfaces of the object with which the person is engaging (e.g., in-vehicle interfaces and assistive technologies) to promote more effective resource allocation management. In the context of driving, this can lead to increased driving safety. More specifically, there is a need for robust methods central to supportive resource allocation systems, and for methods capable of assessing in real-time a person's situation awareness, accounting for factors that both positively and negatively enhance the person's situation awareness. Still further, these new systems and methods should be compatible with intelligent and assistive technologies (e.g., objects having some form of artificial intelligence or other learning or adaptive capabilities) to allow the technologies to adjust to improve the net result based on the knowledge of the person's situation awareness. In the context of vehicles, this can include intelligent vehicles or assistive technology systems, including fully-automated and semi-automated vehicles. The improvements, however, do not have to be implemented in or with such “smart” technology, and ideally can be adaptable for use in objects that are not necessarily set-up to have, or have limited capabilities with respect to, artificial intelligence or other learning or adaptive capabilities. With respect to vehicles, for example, new systems and methods preferably would have the ability to allow for the improved systems and methods to be provided by a plug-and-play, retrofit, or other means of set-up for incorporating safety systems and methods in vehicles, including those vehicles that do not have, or have limited capabilities with respect to, artificial intelligence or other learning or adaptive capabilities.